Analysis of the determinants of sustainable agricultural technologies adoption in tea production in China: a systematic review

ABSTRACT

Agriculture in China is undergoing a swift transformation to modify labour relocation. This change is spearheaded by commercial farms and other Chinese government interventions thereby promoting agricultural production and rural growth. The input-dependent industrial model of agriculture is also considered in conflict with attaining a safe and healthy environment for its inhabitants. The current approaches initiated in the tea sector have witnessed several enriched sustainable agricultural technologies (SATs). The SATs seek to increase yield while ensuring agricultural sustainability via ecological strategies. However, reports have shown farmers have low desire resulting in a lower acceptance rate of SATs among Chinese farmers. We provide a systematic review with a focus on the key determinants of SATs adoption rate in tea production in China from 2000 to 2022. The results revealed the significance and suitability of different approaches in establishing farmers’ adoption behaviour. The specificity of farmers’ SAT adoption behaviour is essentially not compromised by any codified variables but the interrelationship among the determinants, including socio-demographic, agroecological and environmental, technological, institutional, and social networks, and informational, and farmer behavioural factors. We recommend that the government increase awareness of SATs to promote public demand for agricultural sustainability, particularly in developing countries.

KEYWORDS:

• Environmentally friendly
• ecological
• sustainable agricultural technologies
• green technology
• tea production

1. Introduction

Agriculture plays a vital role in sustaining human lives and the environment. Sustainable agriculture is a vital aspect of modern farming practices, and its adoption is critical to ensure the longevity of agricultural productivity and protect the environment (López-felices et al., 2023). Agricultural sustainability is equally important due to the rapid increase in human population, particularly within the last century, and the compounding effect of climate change on agricultural productivity (Dutta et al., 2020; Fernandez-cornejo & Wechsler, 2012) for the lower-input or organic system of farming to sustain the growing world population there is a need for technology to support agricultural productivity. This is further supported by the usage and depletion of natural resources (land, air, plants, soil, and animals) by humans that are higher than what the earth can restock by itself (Sitthisuntikul et al., 2018). Accordingly, the sustainability of agriculture and environmental conservation has become a major concern (Grabowski & Self, 2023). Thus, attracting and awakening the interest of individuals, institutions, organizations, and policymakers (Tey & Brindal, 2012; Waseem et al., 2020). Thus, the foregone conclusion is that the use of technologies in farming would turn around agricultural productivity (Kouchaki-Penchah et al., 2017; Rao & Wang, 2021). Several agricultural technologies are considered sustainable. However, its application on farms is mostly questioned by advocates and this concern is on the rise (Filho, 2018; Kassam & Brammer, 2016; Li et al., 2021; Wang et al., 2018). Hence, the adoption of sustainable technology should aim at reducing the negative impact on the environment in an attempt to achieve sustainability (Niu et al., 2022; Chiriacò et al., 2022; Hu et al., 2018; Tura & Ojanen, 2022). As most of the models employed in conventional agriculture are input-dependent and usually seen as unsuited for attaining and maintaining a completely natural and communal environment (Xu et al., 2021). Sustainable agricultural technologies (SATs) adoption aims towards sustainable cultivation with utmost priority on environmental and health concerns that are acceptable to health-conscious consumers (Abdusalam et al., 2020; Lamboll et al., 2021; Twongyirwe et al., 2020).

Essentially, the progress of time has witnessed various improved technologies and its applications in the tea industry with the purpose of improving the yield and quality of farm produce (Haggar & Rodenburg, 2021). Nevertheless, many of these SATs have resulted in environmental imbalances which influence the growth and productivity of tea (Wang et al., 2021; Mohan, 2020). The assessment report on technology’s impact on the environment shows the harmful effects of technology on natural ecosystems (Hara et al., 1978; Lou & Li, 2021) and the likelihood of worsening income inequality among farmers (Ding et al., 2011; Lou & Li, 2021). Additionally, certain agricultural production technology activities are prime to non-point source pollution through the eutrophication of river bodies (Wang et al., 2021). Moreover, the adoption of SATs in tea production in China has been relatively slow, leading to environmental degradation and economic inefficiencies. Similarly, the rationale for sustainable agricultural technology adoption is not explicitly codified in literature as there are many literatures postulating different benefits relating to economic, non-economic and psychological benefits (Chen et al., 2020; Yan et al., 2021; Wachenheim et al., 2021). Besides, different technologies are applicable to different farms, which may account for adoption variation (Azman et al., 2013; Bui & Nguyen, 2021; Meijer et al., 2015). For instance, Pello et al., 2021 revealed that land topology is a crucial basis for determining adaptability to changes in climate via agroforestry (Yang et al., 2022). Similarly, Fuentes-Llanillo et al. (2021) attributed adoption decisions to the influence of meso-institutions. Young (2015) equally argued that social systems are a critical element in the diffusion of innovation and technology. Given that, the structure of social systems influences the adoption rate by either impeding or facilitating the diffusion. Other studies (Filho, 2018; J. Li et al., 2020a; Yadav & Naagar, 2021) have reasoned that farmers make technology adoption decisions based on how it will reflect in their net profit. Considering the profit maximization concept, several economic models are suggested to analyse the drivers of farmers’ technology adoption, with reference to uncertainty, risk, farm size, credit, farmers’ educational level, financial limitations, and access to information (Koner & Laha, 2021; Mahama et al., 2020). However, ascertaining farmers’ decision-making method is complicated as it is challenged by their livelihoods, decision-making process, and net profit (Lienhard et al., 2020; Tobias Ochieng et al., 2021) Moreover, the economic gains and the environmental consequences of SATs are dependent on the crop and other agroecosystems (Tobias Ochieng et al., 2021). For this reason, no single SATs technique could be able to serve the same purpose and benefit for every producer. Therefore, farmers may be reluctant to adopt a technology despite its associated benefits. On the other hand, farmers may be willing to approve a sustainable technique even though the economic gains are unclear for a short period (Lee et al., 2021). For instance, farmers adopted chickpea cultivar in Ethiopia with contempt without improvement in their yields (Massresha et al., 2021). In this regard, Li et al. (2020) contended that, it is difficult in ascertaining SATs approval decisions by farmers.

Also, international organizations and consumer rights groups have specified the possibility of obtaining a consensus on agricultural sustainability (AS) by accepting strategies, methods, and technologies that embrace unified management procedures leading to ecological integrity (Mahama et al., 2020; Winowiecki et al., 2021). Therefore, many agricultural policies on sustainability approve strategies such as conservation practices, rotating crops, tillage practices, application of integrated pest management, and agroforestry systems (Pretty et al., 2010; Ivezić et al., 2021). These strategies are employed and initiated for protecting the environment, biodiversity, water, among others. These in some cases have yielded results while others do not yield results (Xu et al., 2021). These discrepancies in the literature require empirical research to test the efficacy of these sustainable practices strategies, adoption decisions, policies, and measures, and thus make recommendations to stakeholders (Chen et al., 2020; Senthilkumar et al., 2020). Therefore, understanding the determinants of the adoption of SATs in tea production in China is of importance. It is also important to review farmers’ adoption decisions to be conducted to understand whether or not the determinants meet stakeholders’ intentions, and how farmers’ behaviour affect the progress of AS. Prior studies on SATs adoption have mostly been empirical, using numerous models with key focus on technological characteristics, psychological, and social factors (Li et al., 2021; Zheng et al., 2019). Nonetheless, to the best of our knowledge, no study has combined aforementioned factors to codify the literature on farmer’s SATs adoption decisions in the tea sector in China.

Following such understanding of the gap in literature, the driving force of this review is to codify the determinants of farmers SATs adoption decisions by exploring from different perspectives for effective agricultural, environmental, and national policy-making. The study is unique by focusing on the tea industry (the largest cash crop production) in China, where the sector is considered moderately production intensive. Moreover, unlike other review papers (Lee et al., 2021; Olum et al., 2020; Poškus et al., 2021) that studied the adoption of one specific agricultural technology, this review in its distinctiveness, is a general adoption of SATs as an organized topic for overall understanding, and for policy making at different levels and to different stakeholders. The study results may increase our understanding as well as supporting policymakers in advancing applicable policy interventions aimed at achieving sustainability in agriculture, particularly in the tea sector. Also, this study serves as a framework for other developing tea growing countries. It will add to the body of knowledge specifically on the theory of agricultural technology adoption and seeks to build themes of framework for the determinants of farmers’ adoption decision.

2. Methodology

2.1. Review protocol

This study applied the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework by ( Arhin et al., 2022; Moher et al., 2009, 2015) to identify literature related to elements of SATs adoption in tea production. The PRISMA framework is an efficient and rigorous method of selecting and analysing literature to find potential research gaps for future insight. To identify applicable literature, an inclusive search was carried out in the databases of Science Direct, Scopus, Web of Knowledge, Web of Science, and Wiley Online library. Distinct attention was paid to technical words such as ‘eco-friendly, green technology, precision agriculture, cleaner production, ecological, pro-environmental, and sustainable production technologies’. Implementing the PRISMA framework involves research protocols such as Search Plan, Selection Measures, Quality Evaluation, and Data Extraction. Every stage includes further phases such as literature identification, selection, suitability, inclusion, and exclusion as further explained beneath.

2.2. Search strategy

The researcher followed the procedure outlined by (Durach et al., 2017) for systematic literature review (SLR). Based on the objectives, we developed distinct keywords and personalized them into the databases. We enhanced the literature search with four rounds of keywords. Consequently, we revised previous contents and added different keywords to the evolving collection thereby, enhancing the quality of our review.

Round 1: The phrase ‘sustainable agricultural technologies’ was used. Followed by skimming the results and analysing the referenced articles. We apprehended the need for supplementary word combinations. Thus, a larger set of keyword combinations was identified based on prominent methodologies and theories. We also used keywords commonly used to describe sustainable technology such as ‘green, ecological, environmentally friendly, eco-friendly, pro-environment, technologies’.

Round 2: We applied the Boolean search during this phase using ‘sustainable agricultural technologies’ AND ‘adoption’ AND ‘tea production’, ‘eco-friendly technologies’ AND ‘adoption’, ‘green technologies’ AND ‘adoption’, ‘environmentally friendly technologies’ AND ‘sustainable production technologies’, AND ‘ecological technologies’ AND ‘adoption’, ‘Green production’ OR ‘Sustainable production’ AND ‘adoption’ AND ‘tea production’, ‘environmentally friendly technologies’ AND ‘adoption’ AND ‘tea production’.

Round 3: Using Google Scholar, SAGE, Emerald, and Springer link we carried out the final phase to identify explicitly published papers containing the terms ‘sustainable agricultural technology adoption’, ‘eco-friendly technology adoption in Chinese tea production’, ‘environmentally friendly technologies in tea production in China’, and ‘ecological technology adoption in tea production in China’, as found in either abstract or titles without language limitations.

Round 4: Researchers did a thorough search through the references list of the selected papers to see if any essential paper has been left out. These reference lists were matched against the selected paper. The titles of the published papers were checked through the bibliography.

2.3. Selection criteria

We set inclusion and exclusion criteria by focusing on literature in the field of agricultural science, food, environmental sciences, policy, business and economics, and social science. The selection criteria were opened to China, with a time span between the year 2000 and 2022. The choice of the year 2000 for the study was made because agricultural technology has advanced significantly since that year. These developments could revolutionize tea production, boost productivity, boost yields, and improve tea quality. We can also better appreciate the impact, adoption rates, and possible advantages for tea producers and the industry as a whole by looking at these technologies from the year 2000. The papers in the above-mentioned study fields include field experiments, case studies, mix-method techniques, desktop research, and survey. Excluded papers were not linked to the defined fields, papers outside the time frame (2000–2022), and papers with abstract only written in English language, and papers unrelated to the study objective.

2.4. The quality assessment

The methodology used an enhanced quality assessment stage. By adopting the distinct selection standards and search scheme, the opening keywords search resulted in 465 papers of which 72 were duplicated papers. Also, 313 non-related, and 2 papers without full access were uninvolved. Using the publication year as a guide, 5 articles were excluded. Full-text analysis was carried out on the 73 remaining articles, and 55 of the papers were rejected based on the content either not relating to the tea industry or SATs. Finally, a horizontal and vertical check was carried out by connecting the content to the outstanding 18 articles to the themes of interest. Thus, a total of 18 major papers were preferred for the study. Figure 1 shows the summary.

Figure 1. Review procedure using PRISMA framework.

2.5. The data extraction

The researchers extracted essential information about the 18 selected papers using excel sheets. The extracted details include the names of author(s), research methodology, paper type, paper titles, published year, etc. (See Appendix).

3. Results

3.1. Distribution of articles by journals

The analysis of articles published by journals helps in understanding the topic audience, stakeholders’ interest, and impact of various journals in this field. The 18 papers used were published by 15 Journals as shown in Table 1. Only the Journal of Sustainability has 3 papers representing 20% of total publication, followed by International Journal of Environmental Research and Public Health having 2 papers, representing 13.3% while the remaining journals had only one paper each.

Table 1. Distribution of articles by journals publications.

No.	Names of journals	No. papers	Percentage (%)	Published years
1	Sustainability	3	2.20	2019, 2019, 2020
2	Sustainable production and consumption	1	6.7	2021
3	International Journal of Environmental Research and Public Health	2	13.3	2022, 2022
4	Journal of environmental management	1	6.7	2017
5	Journal of integrative agriculture	1	6.7	2017
6	Journal of Cleaner Production	1	6.7	2021
7	China Economic Review	1	6.7	2005
8	Journal of Tea Science	1	6.7	2017
9	Water Resources and Economics	1	6.7	2021
10	Land Use Policy	1	6.7	2014
11	Agriculture, ecosystems & environment	1	6.7	2002
12	Agroecology and Sustainable Food Systems	1	6.7	2021
13	Soil Science and Plant Nutrition	1	6.7	2016
14	Journal of Cleaner Production	1	6.7	2021
15	World Agric	1	6.7	2020

3.2. Distribution of papers published over time

For the trend of publication in this field, we analysed the number of publications in the last two decades to increase readers’ understanding and reveal to researchers the trend of research activities in the field. Overall, there was a considerably steady growth in publication, particularly within the last six years as illustrated in Figure 2. Interestingly, there is no publication in this field for over a decade (2000–2013), except for the year (2002 and 2005), respectively. The trend however started changing from 2014, climbing more slowly till 2017 with a dramatic increase. The highest publication is in the year 2020 having four publications. This implies that interest in this field has been growing in recent years significantly and shows a positive trend for the sustainability of the industry.

Figure 2. Distribution of papers published over time.

3.3. Representation of research approach

To present the nature of research done in this field, and how it reflects the actual situation of SATs adoption, we conducted analysis on the research approach. As illustrated in Figure 3, the majority of studies were empirical studies with surveys representing 89%, with case study and field experiment representing 5.5% each. This suggests that, majority of the researchers adopt quantitative studies to qualitative studies.

Figure 3. Representation of research approach.

3.4. Author and institutional analysis

The researchers purposefully examined author and institutional affiliation to determine their contributions in this field. The institutional affiliation analysis helps in assessing local and international institutions with interest in the subject matter. The findings show that most papers were co-authored (96%) with few being single-authored (4%). Also, no author has contributed more than one paper to the topic under discussion. The institutional analysis also shows that 70% of papers were affiliated to institutions in China, while 30% were affiliated to institutions across the globe including countries such as the USA, Netherlands, Bangladesh, Spain, New Zealand, Sweden, Egypt, Australia, Canada, Germany, Singapore, Demark, Korea, and France. This implies that local institutions actively play a significant role in the research in this field.

3.5. Theoretical applications in SATs adoption

The concept of technology adoption is common to modern agriculture. Different concepts from psychology, behavioural science, and economics have been used to analyse farmers’ adoption decisions. Studies using socio-psychological theories analyse variables such as intentions, subjective norms, and attitudes (Li et al., 2021). Technology Acceptance Model (TAM) is a commonly used model in the study of agricultural technology adoption (Filho, 2018). It is pivotal in explaining the intent of technology usage by farmers. It does not only consider the psychological variables but also lays emphasis on social influence and the cognitive instrument process on how it affects technology adoption rate (Wachenheim et al., 2021). Moreover, some economic models dealing with variables such as economic risk aversion, price influences, product alternatives etc. are often used in instances where the risk outcome is unknown (Deka et al., 2021; Xu et al., 2022). These economic models also function in terms of the perceived usefulness and ease of technology usage against traditional agriculture practices (Zheng et al., 2019). In addition, economic theories such as the Prospect theory (1979) analyses farmers’ decisions considering economic conditions that link real-life choices moreover optimal decisions. Other studies such as Ding et al. (2011) adopted the environmental protection models and theories such as Lewin’s field theory and Kahneman’s prospect theory to study farmers adoption behaviour. Studies which adopt the environmental protection models are concentrated in areas such as water saving, irrigation technologies, land conservation, and agroforestry (Koner, & Laha, 2021).

3.6. SATs adoption in China

China is the largest developing nation in the world (Huang et al., 2014). Thus, China’s agricultural sustainability and its development offer essential inferences for other developing countries, especially countries that have similar food crop production and agroecosystems (Chun-hua et al., 2023). The government has presented many measures and policies for agricultural sustainability. Some of these measures are for environmental protection through the adoption of SATs. Focus on enhancing agricultural productivity in marginal agricultural lands in rural China is one of the major elements of government agricultural sustainability policy (Ding et al., 2011). For example, the Jinjing government through the Livestock Forbidden Zone Scheme has applied several land use policies to improve environmental efficiency, water quality, and protection of river bodies (Li et al., 2017). Also, since 2005, the government has encouraged the application of formula fertilizers and soil testing technology (FFST). Consequently, the government has spent about 9.2 billion Yuan in encouraging formula fertilizer and soil testing applications in about 2498 counties (Qu et al., 2018, cited in Wang et al., 2018). There is also the promotion of pro-environmental behaviours, farm practices, and technology adoption which focuses on ideas of environmental friendliness, sustainable development, and ecological farming (Xie & Huang, 2021). The adoption of water-saving irrigation technology and integrated pest management systems are likewise common to modern Chinese agriculture (Wang et al., 2021). Similarly, the use of Unmanned Aerial Vehicles is at the primary phase of adoption, aimed to decrease labour works on farms and pesticides overuse (Wachenheim et al., 2021).

Moreover, the involvement of the private sector in agriculture has sensitized the competition for SATs through agricultural producers in China (Thiers, 2005). This has encouraged large-scale farming as a drive for rural development and improvement in agriculture. Nevertheless, lack of motivation from farmers has slowed down the adoption of SATs (Li et al., 2020b), which has led to the continuous worsening of rural lands. Ding et al. (2011) associated the influencing factors for farmers’ low rate of adoption to farmers’ poor socio-economic situations, particularly in areas where SATs are implemented in large portions among low-income households. Given that, most farmers in these areas have less than 3.33 ha of tea farms and are classified as small farms in China.

4. Discussion

Farmers’ adoption decision is difficult to predict due to different factors responsible for the choices. Prior studies on technology adoption have mostly been empirical and as use of models. Individual studies have examined the psychological, social, economic, policy, and environmental characteristics among others. We explored from all perspectives, the different studies and codified the literature on the determinants of SATs adoption. In this review, we grouped the determinants into six classes: (1) socio-economic and demographic factors, (2) agroecological and environmental forces, (3) institutional and social networks, (4) informational forces, (5) farmer behavioural factors and (6) technological factors as shown in Figure 4, following a similar pattern by Olum et al. (2020).

Figure 4. The six interconnected determinants of SATs adop.

4.1. Socio-economic and demographic factors

Socio-economic and demographic factors are significant variables that are stimulated by farmers’ adoption behaviour. They reflect farmers’ socio-economic status and the capacity to adopt new technology (Filho, 2018). For instance, age affects farmers’ behaviour, farm practices, and adoption decisions. The impact of a farmer’s age on the adoption of new technologies can vary based on several variables, including the technology in question, the environment in which the farmer operates, and the farmer himself or herself. Age also shows different characteristics between old and young farmers. Research has shown that younger farmers have higher adoption rate than older farmers (Li et al., 2021). This is frequently linked to younger farmers being more accustomed to adopting new technologies because they were raised in a more technologically advanced society and are more willing to try new concepts. Younger farmers may be more aware of the advantages that new technologies might offer to their farming operations and also tend to have more formal education.

Studies by Chun-hua et al. (2023) show that younger farmers have high appreciation for SATs adoption. This is partly attributed to the habit of young farmers being less conservative than older farmers (Xu et al., 2021). Also, younger farmers have more prospects of benefiting over a longer period than older farmers (Ivezić et al., 2021), and the behavioural characteristics of various age groups. For instance, unlike older farmers who have reduced enticements to change, shorter planning horizons, less exposure to SATs, younger farmers tend to have higher career prospects and are technologically oriented (Mondal & Basu, 2009). Nevertheless, few studies have shown otherwise (Yang & Sang, 2020). For example, Wachenheim et al. (2021) found that older farmers have a higher adoption rate than young farmers. This was attributed to their farming experiences gained from enhanced technologies over time (Wachenheim et al., 2021). It can therefore be said that younger farmers tend to accept new technologies more quickly than older farmers (Niu et al., 2022).

Education is a necessity to aid farmers’ critical and long-term decisions such as SATs adoption. The decision to adopt new technology or improved technology demands knowledge and analytical competence to help farmers comprehend the development (Li et al., 2019). Education influences a person’s cognitive and actions. Farmers’ use of SATs, such as the adoption of innovative farming methods and technologies that support sustainable agriculture, can be significantly influenced by their education and training. The SATs adoption rate is typically higher among farmers with higher levels of formal education than among farmers with lower levels of education. This is because higher education levels are frequently linked to better understanding of scientific theories and procedures as well as a stronger understanding of the need for sustainable agriculture (Qi et al., 2021).. This is confirmed by Rao and Wang (2021) that long periods of training positively influence SATs adoption rate. In this review, the educational level of the household decision-maker is considered to have a positive impact on SATs adoption (Li et al., 2021). Nevertheless, in few scenarios, education had a negative effect on SATs adoption (Yan et al., 2021). The negative impact of education on farmers’ adoption could be linked to farmers having alternative employment or income sources (Sun et al., 2019) which affect their willingness to pay more for improved technology, particularly for the short period (Li et al., 2017).

SATs are frequently made to assist farmers to save and manage resources (water, soil, and nutrients). Farmers may be more inclined to employ SATs when they can increase production levels without depleting these resources. The amount of production per unit area cultivated influences farmers’ productivity and profitability (Li et al., 2009). However, SATs adoption and productivity per unit area do not always correlate directly. In other instances, farmers can be able to produce a lot of food using traditional farming techniques. Thus, they might not immediately notice the advantages of employing SATs. However, it is expected that farmers who can produce more per unit area may be more profitable and have more resources available to spend in SATs, such as new technologies or improved methods. Studies by Zheng et al. (2019) show that farmers with small land; it is suggested that technologies are much more efficient in large-scale farming than smaller farms (Liu & Wang, 2005; Meng et al., 2017a). This is possibly due to the total revenue generated from large production which are far more than small production and channelled into improving production (Yan et al., 2021). Also, Deng et al. (2020) stipulate that, farmers with large farm size have more feasible necessity for technology adoption, due to the comparatively cheaper cost of acquiring technology to enhance the overall productivity of the farms than farmers with small portions of land. Farmers with small farm sizes adopt SATs, having the expectation that SATs will enhance their productivity and increase their yields.

Other socio-demographic forces identified as highly significant in literature include gender, family size, and percentage of income from agriculture influences SATs acceptance (Li et al., 2017). Gender has a significant impact on farmers’ adoption of SATs. This is due to issues such as gender disparities in access to information, resources, and decision-making power which are essential for promoting sustainable agricultural practices (ul Haq & Boz, 2018). For example, gender differences in land ownership and control have influenced the adoption of SATs. In many societies, women have limited access to land, which can limit their ability to invest in new technologies (Deng et al., 2020). Also, due to social norms and cultural practices, in some societies, women may not have the same decision-making power as men, which can affect their ability to adopt new technologies. In addition to possibly having less information and resources available to them than their male colleagues, challenges women in adopting new technologies. Family size relates to many variables such as labour for farm work, sources of income, and family dependency ratio. It is largely suggested to be significant and influence SATs adoption (Ding et al., 2011). For example, farmers with larger families may occasionally be more prone to use SATs since they have more labour on hand to execute the technology. This is especially important for labour-intensive practices like agroforestry or intercropping, where there is a lot of manual labour involved in implementation. But in limited cases family size negatively affects SATs adoption rate (Zheng et al., 2019). This could be attributed to circumstances where large families have more possibility to pursue non-farm activities (Yang & Sang, 2020). Also, due to resource constraints and increasing pressure to continue using their current agricultural methods to support their families, farmers with larger families may be less inclined to adopt SATs. For instance, a farmer may rely on high-input farming techniques to ensure sufficient yields to feed their family if their landholding is small and they have a large family, even though this may have a severe influence on the environment. Another prominent socio-economic variable is the influence of household head’s income levels (Wang et al., 2018). Income level is considered having a positive influence on SATs adoption. Accordingly, farmers who are considered wealthier have relatively higher likelihood for adoption (Li et al., 2019). This finding aligns with the basic economic concept which states that a rise in per capita income will increase farmers’ purchasing power.

4.2. Agroecological and environmental factors

Agroecological and environmental factors examine the dynamic and complex agricultural ecosystem (temperature, rainfall, sunshine, and wind) essential for crop growth and productivity (Deng et al., 2020). Recent information and awareness on climate change have made farmers more knowledgeable about the consequences of climate change (Zheng et al., 2019). Consequently, farmers’ agricultural practices and decisions for SATs adoption are also influenced by climate change patterns (Chen & Chang, 2015). For example, farmers are mostly fascinated by SATs, especially those that deal with climate change and adaptation, land topology, water storage, and conservation (Li et al., 2021). Depending on the climate they are working in, farmers may need to adopt different SATs. For instance, to save water, farmers in arid areas might need to employ drip irrigation. The slope of the land can impact the effectiveness of certain SATs. For example, contour farming can be effective in reducing soil erosion on sloped land. Similarly, depending on the pressure from pests and diseases attacking their crops, farmers may need to employ various SATs such as using natural predators and alternating crops to avoid build-up of disease in the soil. Farmers’ decisions regarding the use of SATs may be influenced by market demand for sustainable goods. Farmers may be more inclined to implement SATs to meet consumer demand if there is a market for commodities that are sustainably produced and are required to meet organic certification. Again, the farm size available to the farmer is a procurator for measuring the economic profit of the farmer. This suggests that commercial producers have a larger capability to handle the prices and profitability over smallholder farmers. Likewise, large farms can spread risks and costs over larger production than smallholder farmers (Meng et al., 2017). Additionally, environmental adaptability with regard to previous environmental conditions of the farm such as earlier shocks from the rainfall, temperatures, humidity, pressure, and health dangers among others are determining inducement for farmers’ adoption decision (Zheng et al., 2019). Considering the effect of risk on human behaviour particularly uncertainties in decision-making. Risk is a crucial factor that influences a farmer’s adoption decision, especially when considering new products or technologies. The perceived risk of a new product or technology significantly affects whether a farmer chooses to adopt it or not (Sun et al., 2019). Essentially, farmers are not subject to only economic menace but likewise the environmental dangers associated with their adoption decision (Li et al., 2021). To avoid high uncertainties, farmers usually make preference between adopting SATs or choosing alternative solutions. Nevertheless, farmers’ choices affect their productivity, particularly the risk of yield loss, and higher cost (Xu et al., 2014). Thus, the greater the perceived risk associated with a product or technology, the less likely an individual is to adopt it (Massresha et al., 2021). Risk levels can however be minimized through legal regulation, voluntary collective actions by farmer cooperatives, training, and extension services (Ivezić et al., 2021). There are several types of perceived risk including financial, social, time, performance, and psychological risks that can impact adoption decisions (Meng et al., 2017). For instance, farmers may perceive the cost of adopting SATs as high, which can create a financial risk for them. This risk may cause them to delay or avoid adoption. Farmers may also perceive that the SATs may not perform as well as advertised. Farmers may at times also perceive that adopting SATs may negatively affect their social standing or relationships with others, particularly when they are part of a group against the intervention (Deng et al., 2020). Farmers may perceive that adopting a new product or technology may lead to negative psychological outcomes, such as anxiety or stress due to lack of knowledge and information on SATs. Farmers may believe that implementing SATs will take a lot of time or effort, which presents a time risk for them (Tan et al., 2021). Moreover, the certification, informational sources, policies, and strategies on SATs could be improved to enhance government technology adoption implementation programmes. For instance, in developing countries where farmers’ level of education is low, it is critical to offer extensive education to farmers on the need for SATs adoption (Li et al., 2021). The education should consider evaluation of farmers’ knowledge on SATs, usage, and other supporting services such as information on cost of new technology, cost and benefits analysis, implementation policies, and market availability (Rao et al., 2021). This will bring about farmers’ trust and confidence in the adoption decision (Tan et al., 2021). Therefore, to increase the adoption of SATs, it is essential to address the perceived risks associated with them. Stakeholders can minimize perceived risk by providing clear information about the benefits and risks of the SATs, providing easy access to farmer support, and offering guarantees or free trials. By minimizing perceived risk, stakeholders can increase the likelihood of adoption among potential farmers (Li et al., 2011).

4.3. Institutional and social networks

Institutional and social network factors are enablers or disablers of farmer’s predisposition towards SATs adoption. Previous studies (Sun et al., 2019; Paull, 2008) have shown that institutional and social networks including government policies, farmer-based organizational activities, cooperative associations, supporting institutional policies, and infrastructure are key determinants to farmers’ adoption decisions. First, institutional networks are the formal entities and structures, such as government agencies, research institutes, and NGOs, that offer services to farmers (Vinholis et al., 2021). These networks can offer farmers access to financial, technical, and training on how to utilize SATs, and its related information. The tools and information offered by the networks position farmers to be more linked and inclined to adopt SATs. Also, social networks refer to the informal relationships and interactions between farmers, such as through family, friends, and community organizations. Hence, influence SAT adoption by providing information and peer pressure. For instance, farmers may be more likely to adopt SATs if they see their neighbours or peers successfully using them. Also, social networks can help overcome barriers to SAT adoption, such as lack of access to credit or markets. By sharing resources and knowledge, social networks can help farmers access the resources needed to adopt SATs. Also, Wang et al. (2018) argued that farmer cooperative is essential in SATs adoption because it influences the impact of group decision.

Funds acquisition is another variable which impact SATs adoption. Limited credit access increases farmers’ level of risk aversion, creates low purchasing power, and negatively impacts on adoption decisions (Li et al., 2021). For example, in China subsidies significantly stimulate farmers’ SATs adoption as a strategic means of meeting farmers’ limited credit sources. The government of China provides subsidies in many forms such as price reduction, and inputs incentives. Studies conducted by Zheng et al. (2019) found that different subsidies receive different reactions from farmers. However, Xu et al. (2021) found that the source of subsidies have no significance in the adoption decision. That is, farmers attach less prominence to whether subsidies are provided by the government, international organization, or farmer-based organization. This implies that policymakers should choose subsidies which attract farmers to adopt rather than subsidies that aim at providing least cost (Chun-hua et al., 2023).

Social identity refers to the way individuals perceive themselves and their belongingness to particular social groups. Group norms affect the uptake of SATs by establishing expectations for behaviour and generating social pressure to fit in. Farmers prefer to be associated with organizations. Thus, group interest becomes the acceptable standard (Ali et al., 2023). Social identity also facilitates collective action, which can be an important driver for SATs adoption. For example, social networks (relationships with fellow neighbours, presence of village political units within the family, cooperative membership, and borrowing channels) of farmers influence their adoption (Wachenheim et al., 2021). In this review, emphasis on social networks is placed on the size, heterogeneity, density, social network types, and convergence of the farmers.

4.4. Informational factors

Information is an essential part of every society. It allows individuals to make choices and decisions. The value of information in technology adoption is equally important in measuring why and how technology is adopted. Essentially, when and how information is transmitted i.e. the ‘medium’ and how information is interpreted (decoded) greatly influence farmers’ technology adoption. For instance, in communities where farmers are considered to have low levels of education, the dissemination of information by extension officers and through farmer-based organizations helps farmers make agricultural decisions (Ivezić et al., 2021). Therefore, inappropriate information transmission can distort the process of technology adoption. On the other hand, there is a higher likelihood of technology adoption when farmers receive the right information at the right time and through trusted mediums or channels such as government agencies and cooperatives (Liu & Wang, 2005).

Again, farmers’ willingness to accept new technology might be influenced by information about their availability and the adoption support systems that are in place. The decision to use SATs by farmers may be influenced by information on their accessibility, availability, and usage. Delays or lack of information on new technologies lead to distrust and hesitation in farmers’ technology adoption. This is because farmers rely on information provided by third parties to form a network of opinions among themselves. As rational beings, they consult, verify, and compare information received to previous experience in similar situations. Hence, information accessibility, availability, authenticity, channels, and quality have significant influence on SATs adoption (Yan et al., 2021). For instance, information availability and accessibility on suppliers, extension services, credit from financial and non-financial institutions, and unique features of SATs affect farmers’ decisions (Yan Wang et al., 2018). Subsequently, rumours, high expectation, and uncontrolled information speculations could consequently affect technology adoption. Government must provide authentic sources for obtaining information including advisory bodies, buyers, credit suppliers, farmer unions, and membership of producer groups (Li et al., 2020). Also, farmers need to be aware of the potential dangers and difficulties that come with applying SATs, such as the input cost, the difficulty of integrating new techniques, and the unpredictability of results. Farmers can use this knowledge to plan and make well-informed decisions.

4.5. Farmers behavioural traits

Farmers’ behavioural traits are displayed in many aspects of farming decisions including choice of farming system, pesticide usage, adoption decision, networks, and members of association. The farmer behavioural traits significantly impact on technology adoption decisions. Even though there are limited studies on farmers’ behaviour and intrinsic motivational forces that could enlighten farmers’ desire for SATs, there are yet few studies which show that behavioural variables such as beliefs, attitudes, and norms affect adoption decisions (Li et al., 2021). Equally important in the farmer’s behavioural traits are variables such as farming experience, risk, perception, and awareness. These variables influence the expectation and knowledge capabilities of the farmer towards the new technology to be adopted. For example, farming experience in terms of how long the farmer has been involved in agricultural production, and used an existing technology is considered significant for the adoption of new technology (Wachenheim et al., 2021). It is held that more years of experience from technology usage and farming foster quality knowledge of spatial variability of technology adoption in agriculture, and to operational efficiency where farmers can learn to adopt new or improved technology (Wang et al., 2018).

Moreover, farmers’ perception influences behavioural intention. Farmers that are risk-averse may be reluctant to adopt new technology until they have proof that they are dependable and capable of generating regular results. Farmers might, however, occasionally be dubious of new technology if they do not fully get how they operate or if farmers have had bad experiences with related technologies in the past. Therefore, the higher the supposed behavioural control, the greater the farmers’ intent of implementing SATs. For instance, even if a tea producer embraces SATs, his supposed capability and threat would impact his behaviour and practice (Li et al., 2021). Thus, influencing farmers’ behavioural intentions especially when the behaviour in interrogation is under volitional regulator regarding risk aversion, awareness, and perceptions will have significant positive influence on SATs adoption. Practically, in communities where farmers perceive a high-level risk attached to SAT adoption, it could negatively impact on the adoption. Also, farmers characterized as risk-averse groups have high likelihood of adopting SATs over farmer groups who are less risk preference (Mwangi & Kariuki, 2015). This is because in instances where farmers perceive easy use of technology. It positively influences the adoption, particularly in communities where farmers are less educated.

Finally, farmers’ attitudes towards the environment and the fact that the new technology is a trend in their vicinity are attitudinal variables that have a strong impact on SATs adoption (Meng et al., 2017a). These attitudes could be influenced by family leadership, membership of an organization, and positive group-membership attitude towards the technology. To enhance the positivity of farmers’ attitudes towards SATs adoption, the government should set up a SATs task force for promotional strategies that targets farmers’ attitude and behavioural intentions (Xu et al., 2021).

4.6. Technological factors

The SATs or ‘green’ or ‘ecological know-hows’ farming is navigating agriculture towards a more sustainable pathway in recent times (Liu & Wang, 2005). The level of farmers’ understanding of the new technology, its effectiveness, affordability, user-friendliness, and provision of high returns compared to the traditional technology impact on the adoption decision (Li et al., 2021). Also, the economic variables associated with the new technology such as cost, guarantee services equally impact on farmers’ adoption. Empirically, the cost and SATs adoption relationship are found to be dependent on the kind of innovation and its elements in the agricultural environment (Qiao et al., 2016). For instance, if SATs are incompatible with farmers’ current agricultural techniques and technology, they would be reluctant to adopt it. The uptake of SATs may therefore be influenced by technological aspects including compatibility with current agricultural practices, knowledge, and communication systems. Accordingly, the concept of traditional technical specification is imperative in understanding SATs adoption as different characteristics of farmers’ adoption behaviour shows positive reflection of the relationship between the features of the technology, and farmers’ adoption behaviour.

Also, farmers’ willingness to adopt new technology is influenced by experience with an existing technology (Tu et al., 2018). For example, the technical risk of adopting new technology and the dependency on other agricultural and non-agricultural resources influence farmers’ adoption. This is predisposed by the adoption effect, policy implications, market drivers, and availability of extension service. Therefore, the ease of use and usefulness ‘applicability’ of technology are considered relevant and positive in determining SATs adoption (Chun-hua et al., 2023). Government must therefore improve farmers’ training on SATs, particularly in communities where farmers are less familiar with agricultural innovations and technologies (Wachenheim et al., 2021).

Again, as new technologies are mostly complex and complicated, adoption of such technologies is likely to be low in absence of extension service, proper training, and adoption information (Liu & Wang, 2005). Government can reduce this technical risk of adoption by providing adequate technical support and extension services, so that the purported use and perceived ingrained use of SATs by farmers will match to reduce doubt and increase farmer willingness.

5. Conclusion and recommendations

Agriculture in China is undergoing swift strengthening due to the changes in its structural and paradigm shift in the workforce. Commercial producers and government are the main forces demanding for agricultural production and its sustainability, especially for the purposes of rural development. Majority of the input-dependent industrial model used in farming does not favour ecological and social environments. With the advent of time, the agriculture production sector has witnessed several developed sustainable farming technologies initiated into the tea industry with the ultimate target of promoting higher yields while ensuring the sustainability of resources. However, reports have shown low interest among farmers for SATs adoption in China. Thus, leading to the constant decline in sustaining the environment mostly in the rural areas.

The overall findings of this review support the usefulness and applicability of various variables as influencing farmers’ decisions to adopt SATs. Even though the determinants of SATs adoption can take in different variables, we grouped the determinant into six categories namely, socio-demographic, agroecological and environmental, technological, institutional and social networks, informational, and farmer behavioural factors. In this context, a significant difference is noted between educated and uneducated farmers. Education influences farmers’ observation, uncertainty, and access to information. Also, family size and income of the family head significantly impact on the adoption decision. While age has been a complex predictor of SATs adoption, nevertheless, most research revealed that, young farmers have higher likelihood to adopt SATs compared to older farmers. We conclude that farmers’ social networks and associations affect the SATs adoption. The specificity of SATs is essentially compromised by farmers’ knowledge about the technology, the cost of the technology, usage experience, and availability of extension services, and information. Finally, farmers’ attitudes towards environmental issues, area under cultivation, experience, availability of workforce, and income of households influence SATs adoption.

We recommend that, for the tea sector to progress in line with ecological footpaths of economic growth, extra production intensifications must be developed through the adoption of sustainable technologies which helps in achieving high returns while promoting eco-friendly production. Moreover, inadequacy of promotional activities towards SATs implementation can limit the development and adoption especially among smallholder farmers. Therefore, the government should increase awareness on the use of SATs to promote public demand for agricultural sustainability. To improve the absorptive capacity, ability, and knowledge capital of farmers on SATs adoption, the government should offer extension services which enhance farmers’ skills, training, education, and networks on the need for innovation and technology adoption in tea production. Agricultural technology adoption decisions are critical and subject to numerous uncertainties. Government should allow demand orientation, decentralization, and market-based needs for technology. This will promote the identification of local farmers’ technological needs and provide corresponding researched technological support for local farmers. Also, owing to the complexity of agricultural technology adoption decisions, providing technical assistance to farmers will enhance their knowledge and understanding of SATs, and improve collaboration and strengthen innovative capacities among farmers. Developing incentive programmes and policies to promote agricultural cooperatives stand a chance of integrating new technologies into tea farming activities and strengthening the functions of cooperatives in SATs adoption. To promote SATs adoption at the local levels, the government must set up local subsidy organizations to stimulate funding of local farm activities and subsidize the private sector on farm activities through fiscal measures. This helps in fostering agricultural innovation and development and allows for privatization of research and extension services for each province or counties. Finally, the government must permit stakeholders such as investors, researchers, funding partners to be involved in the initial priority setting and strategic planning for agricultural innovation and technology development. This may include activities such as giving out incentives and avenues for public–private partnership including consultants, researchers, and international partners to introduce new methods and innovations. Superiority notwithstanding the various research on SATs adoption, there is limited empirical information on farmers’ behavioural and intrinsic motivations that could inform their preference for SATs endorsement. Motivation is key in behavioural studies however, no empirical evidence on the key intrinsic forces influencing SATs adoption, particularly among smallholder farmers. Thus, future works should focus on the intrinsic motivation for SATs adoption, particularly in developing and emerging countries where farmers are mostly considered to be poor and uneducated. Besides the well-known extrinsic factor’s farmers may have different intrinsic motivations. Thus, the findings of such studies will provide empirical-based implications and framework for tea-growing countries that have ventured into SATs adoption for sustainable production. Also, having an empirical study to profoundly explain the differences in farmers’ intrinsic motivations for SATs will offer policymakers greater insight into what strategic actions can be put in place to promote SATs adoption in tea farming. Also, the current study is generally limited to the tea crop in China only. Considering different crop’s characteristics, and farmers’ interest in ecological farming practices, there is the necessity to consider farmers in different crop production in SATs adoption studies. Different crops require different SATs applications. Thus, studies using other crops with different farmer characteristics may find possible interesting findings and add to the literature.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported financially by the Expert Workstation of Yunnan Province (202105AF150045), The Jiangsu agricultural science and technology innovation fund [CX (20) 3006], the Nanjing Special Plan for Shangluo Leading Industry Demand (20201105), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (NJAUPAPD).

Appendix

Table A1. Summary of literature used

Table

No.	Author(s)	Year	Tittle	Model or Theory Used	Method
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2	Li, Q., Zeng, F., Mei, H., Li, T., & Li, D.	2019	Roles of motivation, opportunity, ability, and trust in the willingness of farmers to adopt green fertilization techniques	Structural equation model.	Survey
3	Xu, Q., Yang, Y., Hu, K., Chen, J., Djomo, S. N., Yang, X., & Knudsen, M. T.	2021	Economic, environmental, and emergy analysis of China’s green tea production	A life-cycle-based scenario analysis	Survey
4	Hu, H., Cao, A., Chen, S., & Li, H. (2022).	2022	Effects of risk perception of pests and diseases on tea farmers’ green control techniques adoption.	the Unified Theory of Acceptance and Use of Technology (UTAUT) framework	Survey
5	Li, Z., Li, Y., & Zhang, X. (2020).	2020	Analysis of the Influencing Factors of Sustainable Tea Farming Practice Adoption by Chinese Smallholders.	Gray level co-occurrence matrix and wavelet transform (WT)	Field Experimental
6	Meng, F., Qiao, Y., Wu, W., Smith, P., & Scott, S.	2017	Environmental impacts and production performances of organic agriculture in China: A monetary valuation	Driver-State-Response (DSR) framework	Survey
7	Xie, K., Zhu, Y., Ma, Y., Chen, Y., Chen, S., & Chen, Z. (2022).	2022	Willingness of Tea Farmers to Adopt Ecological Agriculture Techniques Based on the UTAUT Extended Model.	using the extended model of the unified theory of technology adoption and use (UTAUT) based on perceived value.	Survey
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17	Na Wanga, Ying Gaoa, Yonghong Wangb and Xiangfei Lib	2016	Adoption of eco-friendly soil-management practices by smallholder farmers in Shandong Province of China	A logit model	Survey
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